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Open sensing smartphone framework for diabetes management. Andrew Chen, Faculty of Engineering and IT
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Open sensing smartphone framework for diabetes management. Andrew Chen, Faculty of Engineering and IT

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  • 1. Open Sensing Smartphone Framework for Diabetes Management Andrew Ziang Chen Supervisor: Jinman Kim School of Information Technologies FACULTY OF ENGINEERING & INFORMATION TECHNOLOGIES MOTIVATION METHODS • Diabetes affects millions of people worldwide Data input • Traditional methods of management that rely on manual input of data are inefficient • Smartphones are widely available and they have the capabilities to make diabetes management more effective • By utilising built-in functionalities such as access to sensors and other data • As well as their capability of accessing and processing data from anywhere and have social connectivity • Automatic collection • FitBit activity tracker • Exercise information • Sleep monitoring • Smartphone sensors • Smartphone data • Manual input • HealthVault • Account authorisation allows external access to data collected by framework • Health professionals; family • Applications and devices Data output • Timeline overview of daily average blood glucose trend along with data collected. Navigate data by tapping on graph • Glucose level • Diet (Using FitBit food database) • Additional exercise activities AIMS • Create an open sensing smartphone framework for diabetes management that combines smartphones and external sensors to form a sensory system and collect data applicable to diabetes management • Data analysis and output directly on smartphones to provide guidance information for diabetes management Tools • FitBit • Wearable sensor; multiple form factors • High compatibility API • Extensive food database with nutritional information • Alerts and notifications • Customisable by the user • Glucose check, medication, exercise reminders • High carbohydrate intake alert RESULTS CONCLUSION • Effective collection of data using sensory network • An open sensing framework which captures data automatically from sensors, combined with other data commonly available on smartphones, and outputs essential information for effective diabetes management in the form of a companion smartphone app • Data is processed and stored locally and on the cloud for external access • Information provided to user to assist in the management of diabetes • User is able to use the framework to input a wide range of information to be processed along with the automatically collected data FUTURE WORK • Implement the ability for the framework to recognise trends in the data. • Throughout a day of using the framework, a user will: • Discover correlations between the different data types captured by the framework. • Input blood glucose levels • Record meals eaten • Receive processed information and adjust behaviour accordingly for the future THIS RESEARCH IS SP • Introduce integration with glucose meters to achieve faster and more convenient input of blood glucose levels • Undertake clinical trial to evaluate the effectiveness of the framework

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